We build governed AI systems for operational teams: secure agent runtimes, enterprise knowledge platforms, compliance intelligence workflows, and source-backed automation.
FAQ
Straight answers
before the call.
A quick reference for how Anubis Labs scopes governed AI systems, handles sensitive data, and approaches deployment.
The system design starts with evidence, permissions, policy gates, approval paths, logging, and deployment constraints. The model is only one component inside a controlled workflow.
Yes, when the workload and model requirements fit. We support managed cloud, client VPC, on-prem, local-first, and offline patterns with different security and maintenance tradeoffs.
No. Client engagement data is governed by the applicable statement of work and deployment model. We do not use customer data, logs, or PII to train generalized foundation models.
A recurring workflow with clear users, known source material, visible risk, and a decision or action that can be measured. Retrieval, review, triage, and controlled execution workflows are usually strong candidates.
We map the workflow, data boundary, identity model, risks, approval needs, deployment options, and evaluation criteria. The output is a practical implementation path, not a generic AI strategy deck.
That is the point of the practice. We design around auditability, least-privilege access, refusal behavior, deterministic checks, and human review where the risk requires it.
Most pilots are scoped around a small operational workflow and a clear evaluation target. Timelines vary, but a useful pilot is usually bounded enough to show value without pretending the whole enterprise is solved at once.
Still have a specific constraint?
Send the deployment model, data sensitivity, and workflow you are considering. Specifics make the first conversation much better.
Ask about your workflow